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THE USE OF MODERN WEB TECHNOLOGIES FOR ENSURING COMPATIBILITY AND
INTEROPERABILITY IN THE CONTEXT OF MEDICAL INFORMATION PLATFORMS
R.Sh. Garifullin, Bachelor's Degree
Saint Petersburg Electrotechnical University «LETI»
(Russia, Saint Petersburg)
DOI:10.24412/2500-1000-2025-1-3-99-103
Abstract. This article examines modern web technologies that ensure compatibility and integration of medical information platforms, as well as their role in optimizing the processes of data presentation and exchange. The influence of system integration on the interaction between patients, doctors, insurance companies, and administrative staff is studied, enabling the improvement of service quality and reduction of time costs. The application of artificial intelligence (AI) for analyzing medical data, predicting diagnoses, and providing recommendations is explored, opening prospects for personalized medicine and increasing the accuracy of medical decisions.
Keywords: web technologies, medical information platforms, system integration, artificial intelligence (AI), data analysis, diagnosis prediction, personalized medicine.
Modern health information systems are instrumental in aiding the delivery of high quality and timely health services. As technology continues to be developed, the integration of different systems and the development of their interoperability has emerged as a major challenge within the health sector. The key focus area relates to the collection of disparate information about patients, its smooth collation, and appropriate presentation in a form which is easily amenable for analysis and application in patient care by healthcare providers.
Modern web technologies allow the creation of effective methods in the integration of medical information, and its availability and processing ensure the optimality of care. It relieves time pressure and mental loads from health professionals and raises the quality of services at the same time. Moreover, integrated artificial intelligence (AI) brings revolutionary possibilities regarding the examination of medical data, making prognoses of diagnosis, and generating feasible suggestions for it, which improves the accuracy and reliability of medical decision-making greatly. This study aims to analyze the application of modern web technologies to ensure compatibility and interoperability of medical information platforms.
Main part. Modern web technologies for the integration and representation of medical data
The integration and representation of medical data using modern web technologies are im-
portant components of the digital transformation in healthcare. Today, medical information originates from a variety of sources, including electronic health records, laboratory tests, diagnostic imaging, and patient monitoring systems. These data are often presented in diverse formats and structures, making their consolidation and processing a challenging task. Web technologies provide powerful tools to address these issues, ensuring accessibility, interoperability, and high system performance [1].
One of the key technologies facilitating the integration of medical data is the use of data exchange standards such as FHIR (Fast Healthcare Interoperability Resources) and HL7 (Health Level Seven) [2]. These standards enable the standardization of data, simplifying its transfer between different systems. For instance, FHIR allows patient data to be represented in JSON or XML formats, ensuring compatibility with web applications and enabling seamless information exchange between medical institutions.
Cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud play a significant role in the storage, processing, and exchange of medical data. Cloud technologies provide scalability and high availability of medical information, which is especially crucial for large healthcare organizations with significant system loads. Additionally, they enable secure access to data via web interfaces, streamlining workflows for physicians and other specialists.
Modern web frameworks, including React, Angular, and Vue.js, are used to facilitate the effective presentation of medical data through the development of interactive user interfaces [3]. These technologies enable real-time access to essential information, providing healthcare professionals with tools for rapid data analysis. For instance, dashboards built with these frameworks can visualize patient condition dynamics, analyze laboratory test results, or display data from medical devices. This capability enhances the decision-making process and improves the efficiency of healthcare delivery.
An additional essential tool in medical data management is the implementation of Application Programming Interfaces (API), which enable the integration of heterogeneous medical systems. API facilitate the connection of various data sources, such as hospital management systems, laboratories, and insurance companies. By providing unified access to data, API minimize the complexity of interactions between systems, enhancing interoperability and streamlining workflows. Equally significant is the adoption of semantic web technologies, which automate data processing and retrieval. Technologies based on RDF (Resource Description Framework) and OWL (Web Ontology Language) allow for the creation of systems capable of understanding the semantic structure of medical data [4].
Modern web technologies provide robust security and data protection measures, which are pressing in the healthcare domain. Encryption protocols such as HTTPS and TLS safeguard data during transmission over networks. Additionally, authentication and authorization methods like OAuth 2.0 and OpenID Connect prevent unauthorized access to patient data, ensuring compliance with privacy standards.
Said differently, modern web technologies enable data integration processes and open up new possibilities for using such data. Technologies improve the effectiveness of decision-making, reduce the workload of medical staff, and raise the experience of each party within healthcare. The proper use of such tools contributes toward creating a unified digital ecosystem of medical data, one of the biggest steps toward the digital transformation of healthcare.
Optimizing collaboration among healthcare stakeholders through system integration
The collaboration of different healthcare stakeholders, such as patients, medical practitioners, administrative staff, and insurance providers, is one of the key factors to assure the quality of healthcare services. The integration of systems, through modern web technologies, reduces informational gaps, enables real-time sharing of information, and enhances coordination among all the parties involved (table 1).
Table 1. Comparison of stakeholder interaction before anc after system integration [5, 6]
Stakeholders Before After
Patients Multiple visits to various institutions to access and gather their medical records, leading to delays and inefficiencies. Convenient access to comprehensive medical data via personal accounts or mobile applications, improving the experience.
Doctors Fragmented and incomplete patient data across different systems, making diagnosis and treatment planning more difficult. Centralized access to updated and accurate patient information, enabling faster and more precise clinical decisions.
Administrative staff Time-consuming manual processes for appointment scheduling and resource allocation. Automated processes streamline scheduling, optimize resource usage, and reduce administrative workload.
Insurance companies Difficulty in validating and verifying claims due to disconnected systems and inconsistent data. Automated access to validated and real-time data ensures faster claim processing and reduces fraud risk.
One of the major challenges in traditional healthcare approaches is the lack of a harmonious ecosystem of interactions. Patients usually have to visit many facilities in order to get their case histories; moreover, test results are normally lost or duplicated, which leads to doctors not being able to access enough information to make accurate diagnoses. Integration helps solve these
problems. The single access point of medical information platforms provides health professionals with access to up-to-date information concerning the patient's medical history, laboratory results, and current prescriptions. This will expedite both diagnosis and treatment while reducing the possibility of error. To the patients themselves, the integration of the systems brings an opportunity
to actively be involved in their own treatment process. Self-service portals and mobile apps that allow patients to monitor their medical.
Insurance companies also benefit from system integration. By interacting with healthcare institutions through secure API, they can quickly access the necessary data for processing insurance claims. This approach simplifies administrative workflows and reduces the risk of fraud through automatic data validation. Administrative staff in medical institutions gain access to tools for managing patient flows, monitoring physician schedules, and optimizing resource utilization. For instance, automated appointment scheduling systems can dynamically reassign patients among available physicians, ensuring efficiency and reducing wait times.
It also supports system integration, thereby making the implementation of telemedicine technologies possible. Through the growth of tele-medicine, patients can now be treated by physicians while the patient is at home or any remote area. Integrated platforms even allow physicians to share data and make collaborative treatment decisions while working in different health facilities.
This brings a better integration of medical systems, eases interactions between all stakeholders,
Figure
One of the primary applications of AI is in disease diagnosis. Using deep learning algorithms, medical platforms can analyze patient data and identify patterns that may indicate the presence of specific conditions. Medical imaging analysis systems based on convolutional neural networks (CNNs) are already being successfully utilized to detect pathologies in X-rays, CT
reduces administrative expenses, and improves quality in the provision of health services. Establishing such a coherent digital framework has brought medical care more accessible and responsive. It has also made it more qualitatively good, rendering system integration one of the fundamental features of modern medicine.
The application of AI in medical data analysis
The art of AI encompasses a series of technologies that are fast being acknowledged as an integral part of modern healthcare. The sheer volume of information produced from various sources, such as electronic health records, laboratory evaluations, diagnostic imaging, and patient monitoring systems - is fast growing. It has, therefore, started to become clear that traditional methods of data processing are proving to be inefficient in practice. The AI technologies address these challenges through the use of advanced machine learning (ML) algorithms, neural networks, and big data analysis techniques. According to 2023 statistics [7], approximately one-third of healthcare professionals are adopting AI for clinical decision support tools, while one-fourth are utilizing it for predictive analytics and risk stratification (fig. 1).
scans, and MRI. The effectiveness of AI implementation is supported by research conducted in 2021 [8]. In a test set of 279 patients, an AI algorithm analyzing chest CT scans correctly identified 68% of cases, whereas radiologists had classified all these patients as COVID-19 negative.
Another important field of application of AI is prognostics. Different ML algorithms can ana-
10 20 30
1. Share of health systems and hospitals that reported the following use cases of AI were priorities for the short term in the United States as of 2023, %
lyze a patient's past data to determine the risk factors for developing such chronic diseases as diabetes, cardiovascular conditions, or oncological disorders. Predictive models support health professionals in taking preventive measures and designing personalized treatment plans.
The technology of AI is a widespread feature to enable the automation of processing and interpretation of textual data. NLP tools are in use to analyze data residing in electronic health records, patient notes, and medical documentation. The extracted structured information from unstructured text by these tools majorly simplifies data retrieval and reduces processing time.
The application of AI has become very prominent, especially in personalized medicine. In ge-nomics, analyzing vast genomic data with AI could bring new possibilities to develop personalized therapeutic strategies. Genetic information can be analyzed using algorithms that identify predispositions toward certain diseases in advance and prescribe more appropriate treatments for them. In all respects, AI has enhanced the functionality of the medical information systems and performance.
The application of AI in medical data analysis
healthcare quality, enhancing physician efficiency, and optimizing healthcare processes. The integration of AI with modern web technologies enables the creation of intelligent systems that not only streamline data processing but also contribute to the advancement of personalized and predictive medicine.
Conclusion
The application of modern web technologies in medical information systems opens new opportunities for optimization of health care processes, improvement of diagnostics and treatment quality, and better communication between different actors of the health care system. This means that the integration of data made possible by web technologies enables creating a homogeneous informational space accessible and user-friendly for all of its actors. The use of AI has increased the analysis of large amounts of data, predicting potential risks and delivering major recommendations to both physicians and patients. These innovations are speeding up the digital transformation of medicine, making it ever more patient-centered and eventually increasing the efficiency and accessibility of healthcare services.
presents vast opportunities for improving
References
1. Aluev A. Addressing security issues in node.js applications: the economic implications of increased security // International Journal of Humanities and Natural Sciences. - 2024. - Vol. 9-1(96). -P. 94-98.
2. Vorisek C.N., Lehne M., Klopfenstein S.A., Mayer P.J., Bartschke A., Haese T., Thun S. Fast healthcare interoperability resources (FHIR) for interoperability in health research: systematic review // JMIR medical informatics. - 2022. - Vol. 10. № 7. - P. e35724.
3. Ndzimakhwe M., Telukdarie A., Munien I., Vermeulen A., Chude-Okonkwo U.K., Philbin S.P. A framework for user-focused electronic health record system leveraging hyperledger fabric // Information. - 2023. - Vol. 14. № 1. - P. 51.
4. Husâkovâ M. Modelling Medical Information and Knowledge with OWL and Topic Maps // Intelligent Information and Database Systems: Recent Developments 11. - 2020. - P. 265-77.
5. Kozlova M.D., Ogarkov A.I., Kuznetsov I.A., Zalomskiy A.S., Rubin I.M. The Impact of Digitali-zation on Enhancing Operational Efficiency in the Medical Business: Trends and Examples // Competitiveness in the Global World: Economics, Science, Technology. - 2024. - № 5(3). - P. 250-257.
6. Schiavone F., Mancini D., Leone D., Lavorato D. Digital business models and ridesharing for value co-creation in healthcare: A multi-stakeholder ecosystem analysis // Technological Forecasting and Social Change. - 2021. - Vol. 166. - P. 120647.
7. Share of health systems and hospitals that reported the following use cases of artificial intelligence (AI) were priorities for the short term in the United States as of 2023 / Statista. - [Электронный ресурс]. - Режим доступа: https://www.statista.com/statistics/1418039/future-use-cases-for-ai-in-healthcare/ (date of application: 13.12.2024).
8. Huang S., Yang J., Fong S., Zhao Q. Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives // International Journal of Biological Sciences. - 2021. - Vol. 17. № 6. -P. 1581.
ИСПОЛЬЗОВАНИЕ СОВРЕМЕННЫХ ВЕБ-ТЕХНОЛОГИЙ ДЛЯ ОБЕСПЕЧЕНИЯ СОВМЕСТИМОСТИ И МЕЖСИСТЕМНОЙ ИНТЕГРАЦИИ В КОНТЕКСТЕ МЕДИЦИНСКИХ ИНФОРМАЦИОННЫХ ПЛАТФОРМ
Р.Ш. Гарифуллин, бакалавр
Санкт-Петербургский государственный электротехнический университет «ЛЭТИ» им. В.И. Ульянова (Ленина) (Россия, г. Санкт-Петербург)
Аннотация. В данной статье рассматриваются современные веб-технологии, которые обеспечивают совместимость и интеграцию медицинских информационных платформ, а также их роль в оптимизации процессов представления и обмена медицинскими данными. Изучается влияние интеграции систем на взаимодействие между пациентами, врачами, страховыми компаниями и административным персоналом, что позволяет улучшить качество предоставляемых услуг и сократить временные затраты. Исследуется применение искусственного интеллекта (ИИ) для анализа медицинских данных, прогнозирования диагнозов и предоставления рекомендаций, что открывает перспективы для персонализированной медицины и повышения точности медицинских решений.
Ключевые слова: веб-технологии, медицинские информационные платформы, интеграция систем, искусственный интеллект (ИИ), анализ данных, прогнозирование диагнозов, персонализированная медицина.
